SINGLE CELL ANALYSIS
Transcriptional profiling has been revolutionized with high throughput, massively parallel, single cell RNA sequencing. Microfluidic technologies produce comprehensive data sets that allow investigators to understand complex cell mixtures, identify cell types present in healthy and diseased tissues, and create cell type specific transcriptional signatures. These technologies are dramatically enhancing our ability to identify transcriptional and cellular perturbations driving disease and basic biology understanding at the individual cell level.
Uninformative transcripts dominate sequencing reads.
One problem faced by single cell RNA-seq methods is that greater than 90% of single cell RNA-seq dataset is noise and uninformative1. While computational algorithms have evolved to parse out the true signal, commonly available microfluidic technologies enable only sparse sampling of RNAs from each cell, with many genes represented by a few sequencing reads. This limit is partially due to an abundance of biologically uninformative RNAs which dominate sequencing reads and limit detection of the moderately expressed transcripts that often drive biological differences between cell types.
What if there was a turnkey molecular solution that removes uninformative reads in-vitro, thereby redistributing sequencing reads to unique, biologically relevant transcripts?
With CRISPRclean, you can gain a deeper view of expression profiles of individual cells.
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1 Qiu, P. Embracing the dropouts in single-cell RNA-seq analysis. Nat Commun 11, 1169 (2020).
For the US patent, the patent number is US 10,604,802 entitled Genome Fractioning. The patent publication is available at https://patentimages.storage.googleapis.com/22/2a/ef/6657ca336c9c08/US10604802.pdf
For the EP patent, the patent number is EP3102722 entitled Genome Fractioning. The patent publication is available at https://patentimages.storage.googleapis.com/a7/54/96/52ed494ab92016/EP3102722B1.pdf